April 13th, 2015
February 10th, 2015
A new open-source CFD project have just been published. RapidCFD is a new open-source CFD project that uses NVIDIA CUDA for the entire calculation process which gives a significant reduction in computation time.
- most incompressible and compressible solvers on static mesh are available
- all the calculations are done on the GPU
- no overhead for GPU-CPU memory copy
- can run in parallel on multiple GPUs
Visit RapidCFD project page.
February 10th, 2015
Initially introduced as special-purpose accelerators for graphics applications, graphics processing units (GPUs) have now emerged as general purpose computing platforms for a wide range of applications. To address the requirements of these applications, modern GPUs include sizable hardware-managed caches. However, several factors, such as unique architecture of GPU, rise of CPU-GPU heterogeneous computing, etc., demand effective management of caches to achieve high performance and energy efficiency. Recently, several techniques have been proposed for this purpose. In this paper, we survey several architectural and system-level techniques proposed for managing and leveraging GPU caches. We also discuss the importance and challenges of cache management in GPUs. The aim of this paper is to provide the readers insights into cache management techniques for GPUs and motivate them to propose even better techniques for leveraging the full potential of caches in the GPUs of tomorrow.
Sparsh Mittal, “A Survey Of Techniques for Managing and Leveraging Caches in GPUs”, Journal of Circuits, Systems, and Computers (JCSC), vol. 23, no. 8, 2014. WWW
November 8th, 2012
Recent years have witnessed a phenomenal growth in the computational capabilities and applications of GPUs. However, this trend has also led to dramatic increase in their power consumption. This paper surveys research works on analyzing and improving energy efficiency of GPUs. It also provides a classification of these techniques on the basis of their main research idea. Further, it attempts to synthesize research works which compare energy efficiency of GPUs with other computing systems, e.g. FPGAs and CPUs. The aim of this survey is to provide researchers with knowledge of state-of-the-art in GPU power management and motivate them to architect highly energy-efficient GPUs of tomorrow.
Sparsh Mittal, Jeffrey S Vetter, “A Survey of Methods for Analyzing and Improving GPU Energy Efficiency”, in ACM Computing Surveys, vol. 47, no. 2, pp. 19:1-19:23, 2014. [WWW]
September 20th, 2012
The 21st High Performance Computing Symposium (HPC 2013), devoted to the impact of high performance computing and communications on computer simulations. Advances in multicore and many-core architectures, networking, high end computers, large data stores, and middleware capabilities are ushering in a new era of high performance parallel and distributed simulations. Along with these new capabilities come new challenges in computing and system modeling. The goal of HPC 2013 is to encourage innovation in high performance computing
and communication technologies and to promote synergistic advances in modeling methodologies and simulation. It will promote the exchange of ideas and information between universities, industry, and national laboratories about new developments in system modeling, high performance computing and communication, and scientific computing and simulation. Read the rest of this entry »
August 11th, 2012
The Vrije Universiteit Brussel, Erasmus Hogeschool Brussel and Lessius Hogeschool have the pleasure to invite you to a symposium on Personal High-Performance Computing. The symposium aims at bringing together academia and industry to discuss recent advances in using accelerators such as GPUs or FPGAs for speeding up computational-intensive applications. We target single systems such as PCs, laptops or processor boards, hence the name ‘personal’ HPC.
Scientists are encouraged to submit abstracts to be presented at the poster session. All information can be found at https://sites.google.com/site/phpc2012bxl.
June 3rd, 2012
This workshop is concerned with the comparison of high-performance computing systems through performance modeling, benchmarking or the use of tools such as simulators. We are particularly interested in research which reports the ability to measure and make tradeoffs in software/hardware co-design to improve sustained application performance. We are also keen to capture the assessment of future systems, for example through work that ensures continued application scalability through peta- and exa-scale systems.
Read the rest of this entry »
December 19th, 2011
Together with EuroPar-12, the 5th Workshop on UnConventional High Performance Computing 2012 (UCHPC 2012) will take place on August 27/28 at Rhodes Island, Greece. The workshop tries to capture solutions for HPC which are unconventional today but could become conventional and significant tomorrow. While GPGPU is already used a lot in HPC, there still are all kind of issues around best exploitation and productivity for the programmer. Submission deadline: June 6, 2012. For more details, see
http://www.lrr.in.tum.de/~weidendo/uchpc12. UPDATE: Submission deadline extended to June 11.
December 14th, 2011
HOOMD-blue performs general-purpose particle dynamics simulations on a single workstation, taking advantage of NVIDIA GPUs to attain a level of performance equivalent to many cores on a fast cluster. Flexible and configurable, HOOMD-blue is currently being used for coarse-grained molecular dynamics simulations of nano-materials, glasses, and surfactants, dissipative particle dynamics simulations (DPD) of polymers, and crystallization of metals.
HOOMD-blue 0.10.0 adds many new features. Highlights include: Read the rest of this entry »
October 20th, 2011
In this paper we investigate the use of distributed graphics processing unit (GPU)-based architectures to accelerate pipelined wavefront applications—a ubiquitous class of parallel algorithms used for the solution of a number of scientific and engineering applications. Specifically, we employ a recently developed port of the LU solver (from the NAS Parallel Benchmark suite) to investigate the performance of these algorithms on high-performance computing solutions from NVIDIA (Tesla C1060 and C2050) as well as on traditional clusters (AMD/InfiniBand and IBM BlueGene/P).
Benchmark results are presented for problem classes A to C and a recently developed performance model is used to provide projections for problem classes D and E, the latter of which represents a billion-cell problem. Our results demonstrate that while the theoretical performance of GPU solutions will far exceed those of many traditional technologies, the sustained application performance is currently comparable for scientific wavefront applications. Finally, a breakdown of the GPU solution is conducted, exposing PCIe overheads and decomposition constraints. A new k-blocking strategy is proposed to improve the future performance of this class of algorithm on GPU-based architectures.
(Pennycook, S.J., Hammond, S.D., Mudalige, G.R., Wright, S.A. and Jarvis, S.A.: “On the Acceleration of Wavefront Applications using Distributed Many-Core Architectures”, The Computer Journal (in press) [DOI] [PREPRINT])
The new version 3.1 of rCUDA (Remote CUDA), the Open Source package that allows performing CUDA calls to remote GPUs, is now available. Release highlights:
- Fully updated API to CUDA 4.0 (added support for modules “Peer Device Memory Access” and “Unified Addressing”).
- Fixed low level Surface Reference management functions.
For further information, please visit the rCUDA webpage at http://www.gap.upv.es/rCUDA.